Table of Contents
Expected daily cases by region
Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-03-22) in Germany, stratified by region, can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively.
National summary
Summary (estimates as of the 2020-03-22)
| Estimate | |
|---|---|
| New infections | 5764 (3253 – 8030) |
| Expected change in daily cases | Likely increasing |
| Effective reproduction no. | 1.2 (0.9 – 1.6) |
| Doubling time (days) | 18 (6.7 – Cases decreasing) |
| Adjusted R-squared | 0.54 (-0.072 – 0.98) |
Table 1: Latest estimates (as of the 2020-03-22) of the number of cases by date of infection, the expected change in daily cases, the effective reproduction number, the doubling time, and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Reported and estimated cases by date of onset and time-varying reproduction number estimates
Figure 2: A.) Cases by date of report (bars) and estimated cases by date of infection. B.) Time-varying estimate of the effective reproduction number. Light grey ribbon = 90% credible interval. Estimates are shown until the 2020-03-22.Dark grey ribbon = 50% credible interval. Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence.
Time-varying rate of spread and doubling time
Figure 3: A.) Time-varying estimate of the rate of spread, B.) Time-varying estimate of the doubling time in days (note that when the rate of spread is negative the doubling time is assumed to be infinite), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates are shown until the 2020-03-22. Light grey ribbon = 90% credible interval; dark grey ribbon = the 50% credible interval. Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence.
Regional Breakdown
Data availability
- Case counts by date, stratified by region were downloaded from a publically available source (Abbott et al. 2020; Gehrcke 2020).
- Case onset dates were estimated using case counts by date of report and a distribution of reporting delays fitted to a publically available line-list of cases reported internationally (Xu et al., n.d.; Abbott et al. 2020).
Limitations
- Line-list data to inform a region specific estimate of the reporting delay were not available.
Summary of latest reproduction number and case count estimates
Figure 4: Cases with date of infection on the 2020-03-22 and the time-varying estimate of the effective reproduction number (bar = 90% credible interval). Regions are ordered by the number of expected daily cases and shaded based on the expected change in daily cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.
Reproduction numbers over time in the six regions with the most cases currently
Figure 5: Time-varying estimate of the effective reproduction number (light grey ribbon = 90% credible interval; dark grey ribbon = 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Cases with date of onset on the day of report generation in the six regions with the most cases currently
Figure 6: Cases by date of report (bars) and estimated cases by date of infection (light grey ribbon = 90% credible interval; dark grey ribbon = 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22.Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence.
Reproduction numbers over time in all regions
Figure 7: Time-varying estimate of the effective reproduction number (light grey ribbon = 90% credible interval; dark grey ribbon = 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Cases with date of onset on the day of report generation in all regions
Figure 8: Cases by date of report (bars) and estimated cases by date of infection (light grey ribbon = 90% credible interval; dark grey ribbon = 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by shading with reduced shading corresponding to reduced confidence.
Latest estimates (as of the 2020-03-22)
| Region | New infections | Expected change in daily cases | Effective reproduction no. | Doubling time (days) |
|---|---|---|---|---|
| Baden Wurttemberg | 1720 (641 – 2657) | Likely increasing | 1.3 (0.8 – 1.8) | 14 (4.9 – Cases decreasing) |
| Bavaria | 2005 (822 – 3064) | Likely increasing | 1.3 (0.8 – 1.8) | 14 (5.1 – Cases decreasing) |
| Berlin | 225 (80 – 339) | Unsure | 1.1 (0.7 – 1.5) | 29 (5.4 – Cases decreasing) |
| Brandenburg | 117 (41 – 184) | Likely increasing | 1.4 (0.8 – 1.9) | 11 (4.1 – Cases decreasing) |
| Bremen | 26 (1 – 44) | Unsure | 1.3 (0.6 – 1.9) | 18 (3.3 – Cases decreasing) |
| Hamburg | 181 (64 – 287) | Unsure | 1 (0.6 – 1.4) | -570 (8 – Cases decreasing) |
| Hesse | 318 (119 – 515) | Unsure | 1.2 (0.7 – 1.6) | 22 (5.6 – Cases decreasing) |
| Lower Saxony | 512 (200 – 782) | Likely increasing | 1.3 (0.8 – 1.7) | 12 (4.7 – Cases decreasing) |
| Mecklenburg Vorpommern | 47 (6 – 73) | Likely increasing | 1.5 (0.8 – 2.2) | 9 (3.1 – Cases decreasing) |
| North Rhine Westphalia | 1462 (621 – 2356) | Likely increasing | 1.3 (0.8 – 1.7) | 14 (4.9 – Cases decreasing) |
| Rhineland Palatinate | 322 (131 – 522) | Unsure | 1.3 (0.8 – 1.7) | 18 (5.5 – Cases decreasing) |
| Saarland | 161 (28 – 266) | Likely increasing | 1.7 (0.8 – 2.5) | 6.8 (2.9 – Cases decreasing) |
| Saxony | 231 (82 – 358) | Likely increasing | 1.3 (0.8 – 1.7) | 15 (5 – Cases decreasing) |
| Saxony Anhalt | 85 (25 – 140) | Likely increasing | 1.3 (0.8 – 1.8) | 14 (4.7 – Cases decreasing) |
| Schleswig Holstein | 125 (44 – 203) | Likely increasing | 1.3 (0.8 – 1.7) | 14 (4.6 – Cases decreasing) |
| Thuringia | 78 (33 – 131) | Unsure | 1.1 (0.7 – 1.5) | 160 (7.1 – Cases decreasing) |
Table 2: Latest estimates (as of the 2020-03-22) of the number of cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.
Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.
Gehrcke, Jan-Philip. 2020. “COVID-19 Case Numbers in Germany by State, over Time.” Github Repository. Germany. https://github.com/jgehrcke/covid-19-germany-gae.
Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.